Fourthly, the meaning representation can only represent the information in a single sentence, and even by solving the co-reference problem with the Stanford core NLP tool, we sometimes loose or misunderstand important information by removing the context of the sentence.

there is a risk to generalize some facts that are only true in a specific context that is not specified in the sentence.


Coverage is already very high with the first model but deviations from annotated phone boundaries in terms of RMSE continue to decrease with iterations.

: alignment quality continues to increase which leads to better phone-level estimates which leads to better alignment quality.


We have 5 speakers for each dialect, and each speaker of the same dialect converts sentences by themselves.

we have 5 different transcriptions for each dialect, because expression details depend on the backgrounds of speakers even if their dialect categories are the same.


A mismatch between the acoustic model and the acoustic characteristics of user speech is one factor which reduces speech recognition accuracy.

elderly speech frequently contains inarticulate speech, which occurs when the speaker does not fully open their mouth.


The Transcription Bottleneck is a common problem for the documentation projects in under-resourced languages.

the archived audio and video materials are associated only with meta information, including the authors of the recording, usually the data about the speakers and the circumstances of the recordings, sometimes -a summary of the content.


A more general goal is to investigate techniques and approaches for rapid development of speech and language resources and technologies for low-resourced or endangered languages.

we create a reusable infrastructure for language resource development that maximizes the amount of data and resources, at the highest quality of the generated annotations, while minimizing the costs in terms of time and effort invested in the transcription and corpus annotation.


Espeak is a text to speech engine that is used in Praat to generate audio representations from text that are then aligned in a recording, generating hypotheses about the time intervals for the corresponding transcription text.

praat uses text to speech to forced-align transcriptions to an audio recording.


Another point is that Yiddish resources we work on have a number of specific linguistic characteristics that are relevant for the development and evaluation of speech technologies.

yiddish in the recording has a huge dialect-related variation between the individual files.


With the emergence of the trend of applying distributional hypothesis (Firth, 1957) to solve this relation classification task, researchers have started using Distributional Semantic Models (DSM) and have come up with several directional measures (Roller et al., 2014;Santus et al., 2016;Shwartz et al., 2017;Roller and Erk, 2016).

for hypernymy detection, researchers also used a variant of distributional hypothesis, i.e., distributional inclusion hypothesis (Geffet and Dagan, 2005) according to which the contexts of a narrow term are also shared by the broad term.


These works constitute our prime motivation to apply network science methods for co-hyponymy detection.

network features: we propose a supervised method based on the theories of complex networks to accurately detect co-hyponymy relationship.


We used several sources of text data that are publicly available and the Gigaword dataset, as described in Table 1.

we used English Wikipedia from June 2017, from which we used the meta pages archive which resulted in a text corpus with more than 9 billion words 3 .


Understanding sentences requires more than identifying events and participants and giving them thematic role labels.

it is essential to recognize any temporal sequencing within the event and any changes in state that might have occurred (Pustejovsky, 2005;Mani and Pustejovsky, 2012).


Problem (2), the incompleteness of annotation, is a greater challenge.

edges are missing for 9,585 occurrences of subjects and 8,573 occurrences of direct objects (non-oblique cases).


we fit a model predicting reading time from log frequency, one predicting reading time from word length (measured in orthographic characters), and one predicting reading time from log probability under a trigram model.


In this paper, the valence patterns are applied by a rule-based approach.

if a given target word in a sentence is disambiguated as a specific frame-semantics by the target identification task, its frame elements are identified if only it matched with the valence patterns.


word length, which may pre-suppose a particular model, we instead took a functional perspective, with our application domain in mind.

we define word importance for spontaneous spoken conversation as the degree to which a reader of a transcript of the dialogue would be unable to understand the overall meaning of a conversational utterance (a single turn of dialogue) if that word had been "dropped" or omitted from the transcript.


The outline of this paper is as follows: in Section 2 an overview of related work is provided.

we will focus on general gender traits and existing stereotypes as well as on trust and likability in human-machine interaction.


As a cover story the subjects were told that they had to test a new robotic assistant for training which will be deployed in the company they were allegedly working for.

they had to work on five questions per scenario which referred to the specific task scenario at hand.


The concept of personal data is therefore extremely broad; it refers not only, as some may believe, to data containing named entities.

all the unaltered video and voice recordings involving physical persons shall be regarded as personal data, as they often allow to identify the speaker.


If the answer is in the positive, other elements of the DMP are modified accordingly.

: ○ the "Project Description" section:  clearly and unambiguously identifies the data controller (or joint controllers);  contains a statement that if a data processor is involved, he shall « processes personal data only on documented instructions from the controller » and « shall not engage another processor without prior specific or general written authorisation of the controller »;  states the purpose for which personal data are processed and restates the principle of purpose limitation (art.


Let us, first, address pairwise comparisons.

for each language, we train our kNN models on the entirety of the respective data set and then test on all the remaining languages individually (illustrated in Figure 3; resulting in a total of 12 language pairs).


Additionally, we notice a great difference in performance across defusers, underlying once again the individual traits of every defuser.

the selected physiological cues of defusers P100, P102, P202, P203 and P204 appear to be more closely associated to the type of behavioral reply instances (short/long) compared to the corresponding patterns of P101, P103, P104 and P201 defusers.


The goal of this work is to create, for the first time, a large single textual dataset annotated for many emotion (or affect) dimensions (from both the basic emotion model and the VAD model).

we annotate tweets for the emotions of people that posted the tweets-emotions that can be inferred solely from the text of the tweet.


Thus, we created a single common dataset to be annotated for valence, arousal, and dominance, such that it includes tweets from the EI-reg datasets as described below.

the VAD annotation dataset of 2600 tweets included: • From the new EI-reg tweets: all 600 common negative emotion tweets, -600 randomly chosen joy tweets, • From EmoInt EI-reg tweets: -600 randomly chosen joy tweets, -200 each, randomly chosen tweets, for anger, fear, and sadness.


To address the polysemy problem, we propose a method that jointly learns sense-aware word embeddings using both unlabelled and sense-tagged text corpora.

our proposed method can learn both word and sense embeddings by efficiently exploiting both types of resources.


Our proposed method randomly initialises each word w i and each of its senses s ij with unique embedding vectors, and update those vectors such that the rank loss between words and senses that cooccur in unlabelled or labelled contexts is minimised over the entire vocabulary of words.

we do not require sense lexicons or dictionary definitions (glosses) of words/senses in this process.


We used the Rubenstein-Goodenough (RG, 65 word-pairs) (Rubenstein and Goodenough, 1965) dataset as a validation dataset to tune the hyperparameters λ 1 , λ 2 and λ 3 given in (5).

we vary the values of the coefficients λ 1 , λ 2 and λ 3 and learn the sense and word embeddings using the proposed method afore measuring the Spearman correlation on RG dataset.


Overall, from Table 4, we can see that the proposed method reports the best performance results for most of the benchmark datasets.

the proposed method obtains the best results on TR, CR and SUBJ, whereas GloVe obtains the best performance on MR. Table 4 supports the conclusion drawn from Table 3 that it is beneficial to consider a sense-labelled corpus to obtain better word embeddings not only to learn sense embeddings.


In order to benefit from the parallel structure across languages, we aggregate each similarity value and related ICB identifier from each language for a given GICS element.

for each GICS element we obtain four ICB ids and four similarity values.


In this paper, we propose a scheme to represent Gan (Jiangxi province of China) Chinese dialects.

it is a two-level and fine-grained representation using Chinese character, Chinese Pinyin and Chinese audio forms.


Given the above scheme, we annotate parallel 310 XML-style documents with 6 different genres (news, official document, story, prose, poet, letter and speech), containing 218 newswire documents from Chinese Treebank 6.0 with Linguistic Data Consortium (LDC) catalog number LDC2007T36, and other 92 documents for the remaining genres from the internet using Baidu search engine with official document, story, prose, poet, letter and speech as key words.

we don't have parallel sentences for each variant of each sentence in all documents, and the documents included differ among the dialects but are all parallel with respect to a Mandarin translation.


In addition, we developed our own automatic text segmentation system and a part-of-speech tagger, since our texts often contain orthographic deviations, domain-specific structures such as fragments, subject-less sentences, interjections, discourse particles, and domain-specific formulaic communicative routines in salutation and greeting.

we demonstrate that the CRF-based POS tagger could be boosted to a domain-specific text by adding a small amount of in-domain data.


In this paper, we described the process of digitalising, transcribing and annotating of over 11,000 handwritten postcards.

we demonstrated that the POS tagger could be boosted to a domain-specific text by adding a small amount of in-domain data.


A total of 7,961 sentences are annotated with 7,163 frame-evoking lexical units (out of a total of 8,206 candidate lexical units).

there are 3,577 CANCER DIAGNOSIS frames, 2,204 CAN-CER THERAPEUTIC PROCEDURE frames, and 1,382 TU-MOR DESCRIPTION frames.


Herein, we review the extent of parallel corpus coverage in the biomedical domain.

we perform a scoping review of existing resources and we describe the recent development of new datasets for scientific literature (the EDP dataset and an extension of the Scielo corpus) and clinical trials (the ReBEC corpus).


The class selection depends on the outcome of the Intervention compared to the Comparison.


It is important to note here that the word alignment tool Giza++ is used to generate the translation tables for both methods (baseline and our approach).

for the baseline (Giza++), the translation table is generated from the parallel corpus which is the concatenation of the generalpurpose training data (Europarl) and the domain-specific data (Emea).


The TempEval-3 datasets are compliant with the TimeML Annotation Guidelines (Saurı et al., 2006).

an event is defined as any linguistic mention, including verbs, nouns, adjectives and prepositional phrases, which denotes something that happens, occurs, or describes states/circumstances in which something obtains or holds true.


CRF4TimeML has been designed taking as reference efficient existing systems that incorporate discrete classifiers.

all classifiers we developed share with previous systems basic morpho-syntactic features, such as token, lemma, POS, and dependency relations.


With the exception of one system (Bethard, 2013) that uses a restricted set of temporal values, all system outputs are as skewed as in the training data.

systems tend to predict the values BEFORE, AFTER, and SIMUL-TANEOUS for e-e pairs, IS INCLUDED for e-t pairs, and BEFORE, INCLUDES, IS INCLUDED, and AFTER for e-dct pairs.


Despite the nonexistence of statistical difference in larger samples, they present a smaller standard deviation.

the result is more reliable using larger samples.


The development of statistical methods in automatic language processing leads to an increased need for annotated resources.

training algorithms that aim to detect coreference 1 requires manually annotated texts.


The URS metamodel provides a framework to describe relations between linguistic units with additional characterisations, making it straightforward to describe coreference relations, but can be extended to other types of annotations.

describing dependency syntactic analysis is easy, since dependencies can be seen as simple typed directed relations.


The process we describe is iterative -as we will show, at each iteration the previous approach is scrutinised and improved upon, which allows for acquisition of the recordings on a larger scale than possible before, while improving user experience, and thus the quality of the data collected.

we describe a tool designed to improve our Automatic Speech Recognition (ASR) system performance, specialised for conversations over telephone - SAR-MATA (Ziółko et al., 2015).


While the tag proportions are roughly the same between the training and the test corpus (both taken as a whole), as only the PROPN and X are slightly overrepresented, the distribution of tags is not balanced among the evaluation corpora.

13 the category PROPN is proportionally 2.2 times more present in E SV than in the training corpus and is mistaken for NOUN in almost 60% of the cases.


We used three existing parallel text resources as the basis for the sentence selection so as to cover a broad range of online courses and to cover all eleven language pairs.

we use parallel texts from course material of the Coursera MOOC platform, the Iversity MOOC platform, and the QCRI Educational Domain (QED) Corpus (formerly known as QCRI AMARA Corpus) (Abdelali et al., 2014).


Another interpretation of Chronbach's alpha is that it measures internal consistency.

it measures how well items that can be used to measure similar characteristics of test takers would result in similar response patterns of test takers.


Briefly speaking, it shows how well j discriminates low-ability test takers from highability test takers.

if a j becomes larger, the difference in the probability to respond correctly between high and low test takers becomes larger.


(Li et al., 2008) (Zhang et al., 2008), (Zhang et al., 2011) published a series of papers on feature-based approaches.

they designed nine positional structures of enti-ties and focused on the effectiveness of the positional feature on Chinese relation extraction.


11,071 out of 40,168 Chinese Sina Microblogs were labeled uncertain following our proposed scheme with the Kappa value over 0.85 that indicated substantially reliable.

we define six sub-classes for the uncertain classification which made the corpus more detailed and useful for other related NLP applications in the future.


When we finish all the steps above, the profile of the event is built, which can be used as an event entry for an event knowledge base.

the above five steps can be seen as the subtasks of event knowledge base construction.


In this paper, we analyze relationships between word pairs and evaluate their idiosyncratic properties in the applied context of authorship attribution.

on three literary corpora we optimize word pair features for information gain which reflect word similarity as measured by word embeddings.


It can be conceptualized as an extension of the entity linking problem (Erbs et al., 2011;Rao et al., 2013), attempting to link citations to variables in a knowledge base or it can be phrased as a Recognizing Textual Entailment (RTE) problem, where the system should be able to identify whether a sentence entails a given candidate hypothesis or not (Dagan et al., 2013;Bentivogli et al., 2009).


The adaptations to be done mainly affect the extent of temporal expressions.

in German there are compounds which can sometimes contain temporal expressions (e.g., Diskussionabende "Evenings of discussion") and there are contractions of prepositions and articles (e.g., im: in + dem "in the").


We introduce the ACoLi CoNLL libraries, a set of Java archives to facilitate advanced manipulations of corpora annotated in TSV formats, including all members of the CoNLL format family.

we provide means for (i) rule-based rewrite operations, (ii) visualization and manual annotation, (iii) merging CoNLL files, and (iv) data base support.


In consequence, we obtain an isomorphic representation of the original CoNLL data structure in RDF which is semantically shallow, 6 but can be effectively queried, manipulated and serialized back into CoNLL using off-the-shelf RDF technology.

this includes a rich infrastructure of databases, webservices, APIs, models for resource publication and linking (Chiarcos et al., 2013).


The senses in this resource are induced automatically from text via clustering of graphs of semantically related words and linked to BabelNet and Word-Net lexical resources.

in the resource, each sense (e.g., read#VB#1) is defined by its related senses (quote#VB#0, recite#VB#0, and so on) which have been observed to co-occur with the sense.


Lastly, WNP and WNN measure the overlap of the candidate term WordNet synset information with the terms in the positive and negative category, respectively, as follows.

the WNP score of a candidate term c k is computed as the term overlap between the synonyms of c k (Syn(c k )) and the set of expanded terms in k from the previous epoch E(k) t−1 plus the the term overlap between the antonyms of c k (Ant(c k )) and E(−k) t−1 .


Solutions to the questions raised above will require advances in NLP.

we need techniques that go beyond analysis of the happy moments at the keyword level and perform deeper analyses such as semantic role labeling (into possibly a set of frames that leverage Framenet, Verbnet, and/or Propbank).


A χ 2 analysis (Manning et al., 2008) shows that the presence of the 5 positive examples in I 1 does affect the word usage of workers.

our null hypothesis is that the word usage of the happy moments is independent of whether the instruction set contained positive examples or not.


Our current research has utilized the approach and methods discussed in these works.

we use the components of sociolinguistic toolkit to compute and compare influence of all participants in our experiments and then determine how it relates to their knowledge of the discussion topic.


For now it may suffice to understand that this refers to a pruned list (Roelli & Bachmann do not necessitate pruning, but for the application intended here it is necessarily a pruned list) of genealogically indicative variants.

by this method, for each variant it is determined if it can serve as a leitfehler (1) or not (0).


which can be clearly recognized as a target character, many candidates are "incomplete".

(Type-1) just noises, (Type-2) complete one character but contains noises,  In this work, we decided to filter out all of those incomplete candidates except (Type-2) because we can denoise and don't have to adjust aspect of the images again.


The reason why we is to tackle to diversity of cuneiform languages and ambiguity of character glyph.

the latter problem is needed to treat both graphically and linguistically.


Due to the constraints mentioned above more and more researchers have been turning to crowdsourcing as a means of gathering large quantities of data.

crowdsourcing has been extensively used for annotations as well as transcriptions (Gruenstein et al., 2009;Hipp et al., 2013;Rashtchian et al., 2010).


This indicates the difficulty of the sympathy identification task.

the judgment of implicit sympathy tends to be inconsistent.


This may be because the numbers of occurrences of d 8 and d 9 are too small, as shown in Table 1.

there are much fewer positive samples than negative samples.


each of 20 possible referents in a session) but also for individual features, as done in this paper with the categorical feature SHAPE.

not only can RL convergence be measured for individual referents but also for features of said referents, which are thus generalizable to other entities with similar features regardless if they have previously been referred to in discourse or not.


(2) ECA's engagement modeling: the ECA should adapt its behavior in order to maintain the desired level of engagement during the interaction.

this work investigates what are the multimodal behaviors that participate to a change of perception of the engagement level in a human-agent interaction.


Within this level of engagement, the mean starting time of smile is -0.05 seconds, this means that 0.05 seconds before the perception a strong engagement, the expert smiles.

the perception of strong engagement is triggered by the expert's smile.


A projection is a planar representation of a spherical object.

a map projection is an attempt to portray the surface of the earth onto a flat surface.


Crucially, the lack of language points is also not indicated in typological atlases.

there is no visual cue for the absence of data.


However, one problem is that the entire geographic extent is accounted for when we create these polygons on a worldwide scale.

areas with low sampling density will have large polygons radiating far beyond a reasonable distance of influence.


Retention can be defined as the probabilistic likelihood of retaining a linguistic feature in descendant languages from the parent language.

today's descendant languages have a high probability that they share features with their parent languages because they are genealogically related.


The In total, the test and training corpus consists of 822 lines distributed over 268 dialogues.

for each turn we annotated each line with its speaker, its addressee or addressees (compare Section 3.2.


The solution to this case is that we look these words up in Cambridge Dictionary and get the meanings in different Chinese expressions separately.

we find explanations for both bicycle and bike are "脚踏车, 单车, 自行车", and then we choose different Chinese words for bicycle and bike relatively.


While the same translation policy is applied, the translation of thai is "泰国人 (a person from Thailand)".

(croatia, croatian, thailand, thai) is translated as (克罗埃西亚, 克罗埃西亚人, 泰国, 泰国人).


Theoretically, our work is inspired by the observations of (Hodges, 1972).

we do not accept the 13th century description of English spelling as chaotic.


The current study improves the state of the art in cross-lingual word embeddings by considering the impact of orthographic similarity between cognates.

the use of the Weighted Levenshtein Distance combined with orthogonalisation of the translation matrix and generalised correction for hubness can considerably improve the state of the art in induction of bilingual lexicons.


More than one link to any of the additional resources can be included; in such a case, it means that the same or similar meaning has been found in the external lexicons, with an unclear distribution of senses.

mappings between CzEngClass entries and the entries in the other lexical resources are not necessarily 1:1.


For example, 鬼の城公園 was translated as 'Demon Castle Park' since 鬼の城 consists of 鬼の 'demon' + 城 'castle', whereas the actual name of this park in English is 'Oninojo Park'.

鬼 の城公園 was not recognized as a named entity but was translated literally component by component.


Then, when we do the combinations and we update the ID references to the new meaning groups resulting from the combination of two or more existing meaning groups.

if meaning groups with IDs 1, 2 and 3 are combined, we modify the pointers from the lemma entries pointing to 2 and 3 for each language to point to the meaning group 1 which now contains the information from 1, 2 and 3.


After using the algorithm, the number of unique meaning groups dropped to 9914 which means that 3181 meaning groups were merged into other meaning groups.

the amount of unique meaning groups was reduced by approximately 24 %.


Compared with English FrameNet, in which 13,638 LUs, 1,221 semantic frames, and 200,000 annotated sentences are provided, Chinese FrameNet is considerably smaller.

chinese FrameNet has a much low coverage of frames and lexical units, and results in limited applications.


Second, EFLLex includes both words from the core vocabulary that should be taught at a given level, and peripheral words that can be encountered by learners, but should probably be learned at a later stage of the curricu- lum.


To address the under-translation problem, we explore the pre-ordering approach for NMT.

we pre-order the source sentences to approximate the target language word order.


in 500 sentences, NMT system produces 92 under-translations and 32 over-translations.


Then the coverage vector is fed to attention model to adjust the attention in the next step.

more two steps are needed: We need to maintain a coverage vector, which summarizes the attention record at each decode step as follows: where Φ j is the fertility for word x j , and can be computed by Where N is the largest fertility, and U f is the weight matrix.


Here, we only focus on the work which combines the SMT and NMT on NMT framework.

(Arthur et al., 2016) incorporates word translation table in attention part to adjust the final loss.


We have exploited the pre-ordering approach to alleviate the under-translation problem in NMT.

we preorder the source sentences to make their word order more consist with the word order in target.


A model obtained BLEU scores of 27.07 and 12.99 in the TRIP and TED data sets, respectively.

training a multi-source model with a synthetic source can reach the improvement of 5.15 and 6.8 BLEU score for the two test sets.


Since the main difference between training and inference for sequence prediction tasks when predicting token y t is whether we use Figure 1: Scheduled Sampling the true previous token y t−1 or an estimateŷ t−1 coming from the model itself, they apply a sampling mechanism that will randomly decide, during training, whether to use the true previous token y t−1 or an estimateŷ t−1 coming from the model itself.

for every token, they flip a coin and decide whether to use the true previous token or an estimate coming from the model itself.


Minibatch stochastic gradient descent is applied to look for a set of parameters θ * that maximizes the log likelihood of producing the correct target sentence.

given a batch of training pairs {(X i , Y i )}, we aim to find θ * which satisfies: Whereas during inference the model can generate the target sentence one token at a time, advancing time by one step.


To achieve this goal, we borrow the idea from Scheduled Sampling.

during training when decoding, there is a possibility that we feed our model with an estimateŷ t−1 coming from the model itself rather than true previous token y t−1 .


So we propose to learn a specific model for each testing sentence by paying more attention to those related sentences.

we propose a learning on-the-fly strategy for parameter fine-tuning.


Whether its paradigm is statistical or neural, the availability of parallel data is essential for building high-quality systems.

manually verified data sets for development and testing are of great importance for improving and evaluating MT systems.


HunAlign To employ HunAlign, we use an approach similar to the three-step workflow used in the JRC-Arcquis corpus (Steinberger et al., 2016) and DCEP corpus (Hajlaoui et al., 2014), which consists of an initial alignment using length similarity, automatic dictionary construction from the initial alignments, and a second alignment using lexical similarity calculated with the constructed dictionary in the second step.

we first run HunAlign to obtain the initial alignment without dictionary, randomly sample 10,000 sentence pairs from the 1-to-1 segments in the initial alignment, build a dictionary with minimum occurrence score of 2 and minimum association score of 0.2, and finally, re-align all sentences with the constructed dictionary.


Although these methods compensated/improved purely linguistic representations, the source of visual features cannot be limited to image data.

if a content of any modality is described with a substantial amount of linguistic tags and/or descriptions, the linguistic co-occurrence observed around the content can be utilized as a source of semantic features.


In this context, there are several available meaning representations, as the traditional First-Order Logic (FOL), as detailed in Jurafsky and Martin (2009), semantic networks (Lehmann, 1992), Universal Networking Language (UNL) (Uchida et al., 1996), and, more recently, the Abstract Meaning Representation (AMR) (Banarescu et al., 2013).

aMR got the attention of the scientific community due to its relatively simpler structure, establishing the connections/relations among nodes/concepts, making them easy to read.


Researchers in many fields are able to use the multiple search functions on the website for their specific research purposes.

the corpus is useful for researchers focusing on political speeches and conceptual metaphor analyses.


Word sense disambiguation (WSD) involves identifying the senses of words in documents.

the WSD task where the senses of all the words in a document are disambiguated is referred to as all-words WSD.


Finally, we predict the target word senses using the Knearest neighbors (KNN) algorithm, based on the distances between the surrounding word vectors for the target words and their synonyms.

we calculate the distances between the surrounding word vectors for the target word and its synonyms labeled as 1.2410 or 1.5501 and determine the word sense of the target word via KNN algorithm.


In other words, we calculate the distances between the surrounding word vectors for the target word and its synonyms labeled as 1.2410 or 1.5501 and determine the word sense of the target word via KNN algorithm.

if the synonym with label 1.2410 was nearer by the algorithm, the word sense of the target word will be 1.2410 and vice versa.


• Words are excluded if they are synonyms for more than one sense of a given target word.

the surrounding word vectors of A are created not from all word tokens of A, but from only word tokens of A predicted as Sense 1 at the n-1 prediction.


In this paper, we consider several implementations of the feature space to which x belongs.

we partition x as follows where the comma represent the vector concatenation operator.


Another direction we explore is when the semantic features are computed within a multi-layer architecture.

we use the trained WSD model described so far to disambiguate the context of the target words and compute priors on their senses (a related idea was exploited, for example, in (Khapra et al., 2010)), then we update the semantic features and train another model.


They almost always provide improvements over the baseline approaches, even if in different configurations.

in the IMS case, semantic features lead to more evident benefits w.r.t.


Senses mapped to the same concept are considered to be the same candidate; thus, the concept can be utilized in the WSD method.

the sense candidates of the word "sa-gwa" can be classified as shown in Table 1.


For every word which is sense-annotated in a sentence, we add to the definition of this sense in the dictionary every other word present in the sentence.


Because a word maps to multiple sense vectors in this scenario, standard cosine similarity computations alone are not applicable anymore, we thus test a number of sense-aware comparison methodologies based on cosine similarity.

for word pairs involving minor/rare senses, we expect improvements in the sense-aware setting as the influence of the dominating major sense is diminished.


The WORDSIM353 (Finkelstein et al., 2001) dataset provides relatedness scores of 353 noun pairs and the SIM-LEX999 dataset provides similarity scores for 666 noun pairs, 222 verb pairs and 111 adjective pairs.

the latter's emphasis is to model opposite meanings (antonym-like) as highly non-similar, e.g.


The average improvement for most sense-unaware systems to their sense-aware counterpart is roughly between 0.02 and 0.05 points of spearman correlation.


1 Whether a sample is a novel or not was determined by the NDC (book classification), which is meta-information of the sample.

the NDC regards texts that correspond to 9x3 (x=1 to 8) as novels.


Therefore, Miyazaki (2017) created objective criteria and ensured that the annotation work did not become arbitrary.

relevant parts were enclosed with "" and parts assumed to be someone actually speaking in the scene were regarded as conversation.


To test the effectiveness of PDFAnno, we conducted an annotation experiment for IE from scientific papers.

we asked experts in materials science to annotate selected papers in their field with material names and their physical quantities such as temperature and thermal conductivity.


Additionally, our wrapper provides a convenient way to generate the required IOB-labeled sequences 7 of characters from a pretokenized corpus.

the .conllu format used in UD2 (Nivre et al., 2017) is accepted as input, as well as similar formats such as the one used in the 2017 Shared Task on Identifying Verbal Multi-Word Expressions (see §5.4.).


It turns out that most datasets in the UD2 corpus do not benefit from these features: they were selected as part of the optimal template in only 16 cases (16% of the datasets).

none of the CJKV languages benefits from them.


This experiment shows that token boundaries do not only depend on the language, and therefore that applying a certain tokenizer for this sole reason is not always optimal.

the fact that traditional tokenizers are rule-based does not imply that there is a unique way to tokenize a lan-  Table 2: Token-based recall (percentages) when applying a tokenizer trained on a given dataset (rows) to another dataset (columns) in the same language.


In fact, the main difference when increasing the size of the training set is that the variance decreases: the mean progresses as well, but the most important observation that can be made from Figure 1 is that, as the size of the training set increases, all the languages reach a high level of performance.

even if a small training set might suffice to obtain an accurate tokenizer, training it with a large number of sentence makes it more likely to be accurate.


Moreover, the discrepancies between the tokenized input data and the third party data (when tokenized using a generic tokenizer) might prevent matching correctly VMWEs which contain words tokenized in a different way.

vMWEs frequently include function words which are susceptible to tokenization errors, like "c'est" (it is) in French: if the tokenizer does not properly recognize the apostrophe as part of the first token "c' ", 17 expressions which contain "c'est" cannot be matched.


The BFI inventory has been replicated in dozens of other languages, including some studies dedicated to our target language, Brazilian Portuguese.

the study in (de Andrade, 2008) validated the BFI for Brazilian Portuguese through factorial analysis of a sample of 5,089 respondents from all regions of the country.


For reasons of anonymity, and also to provide a minimal level of normalisation, the text was also subject to a number of replacement operations.

proper names were replaced by a $NAME$ identifier, numeric expressions were replaced by $NUMBER$ and laugh and emotion expressions (e.g., 'yay', 'ouch', 'haha', 'LoL' etc.)


Moreover, all descriptions of a given image showing, e.g., a short-haired person (according to the information provided by Face Place) were annotated as hair.length-short even if a particular individual described it as being long.

the annotated value is meant to model the reference to the hair.length attribute, but not necessarily of the actual (short or long) value chosen by each individual.


The Dutch language exhibits different word order in main clauses and subordinate clauses, which also affects the use of bipartite negation.

the verb in a main clause is positioned between the clitic and the negative (hij en is niet, English he [clitic] is not), while in a subordinate clause the verb is positioned at the end (dat hij niet en is, English that he not [clitic] is, meaning that he is not).


Although the vocabulary used in the simple sentences decreased to 25% of the original sentences, both scores are high.

even if the vocabulary was restricted, most of the meaning of the original sentence could still be expressed.


As for the reason that the performance is showed in accumulation, it can prove the stability and feasibility of our approach over literature of different eras (There are unique words and expressions in each dynasty, some of which only appear within a certain period of time).

it's similar to one-size-fits-all approach, with which we don't have to create lexicon for each book.


Research effort in the recognition of affect in text has focussed to a large extent on recognition of positive/negative sentiment, while more nuanced emotion representation models have received relatively little attention.

there has been a lack of quality annotated resources for model building and evaluation in that space (Mohammad, 2016) and in particular with dimensional annotations.


In addition, emotion tags trust, anticipation, fear, anger, and sadness are controversial.

we received a high volume of feedback for emotion tags fear, anger, and sadness, indicating that these emotion tags are confusing, interchangeable, or can be used together for tagging data points.


In this case, the database maker (usually the person or entity who bears the financial risk) enjoys a sui generis database right (SGDR), which protects the content of the database from substantial extractions.

even databases of unprotected facts could become object of a proprietary right that extends to the database content in the light of the aforementioned substantial investment (Hugenholtz, 2016;Guibault and Wiebe, 2013).


The simplicity of the training process outlined above and the limited choices in the parametrisation of a largely automated process suggest that there is no space for the free and creative choices that allow the author to express their personality into the work.

it seems that even when certain choices in parametrisation are available these are dictated mostly by technical considerations and by the "rules of the game" of model training in a way that any equally skilled technician would achieve a similar or identical result.


It seems that the the ML/NLP steps described in scenario three are substantially similar to those described by the EUCJ in the reported case law.

: 1. transforming the text corpus and the annotations into the input format of the Stanford NER tool is arguably equivalent to converting a TIFF image into text using OCR but much less sophisticated; 2. inspecting each word in the text in turn in order to create a ML feature representation capturing from the word and its immediate left and right neighbours, and from the annotation on the word is arguably equivalent to extracting the search term and the words before and after it, although extracting only one word before/after instead of 5, for a total of 3 words instead of 11 words extracted; 3. creating a probabilistic report (i.e.


The elements are carefully selected to capture properties of the resources and relations with other resources that are deemed important for their description and relate to any of the stages of the resource lifecycle, from production to consumption.

relations are encoded in the metadata records of all resources which have gone through some processing, conversion or transformation indicating the type of relation between the two, essentially different, versions of a resource; e.g.


Although we acknowledge that universal interoperability for NLP tools is far from a solved problem, we believe this project takes an important step towards its achievement.

the exercise of pursuing semantic interoperability among the three frameworks has yielded new insights into the nature and source of obstacles to interoperability that could significantly impact future progress towards this seemingly elusive goal.


These new tag information along with the predefined tag data, in turn provided a more probable tag by Fluid Tagger.

deepLexicon expanded new lemma and tag information as annotation process progressed.


The results from pre-segmenter are the segmentation to which other word granularities align themselves.

the patterns from different granularities can merge word sequences produced in pre-segment, but dismissed if the word conflict with the pre-segment results.


The effects of various morphological normalization techniques on STS model performances have also been evaluated.

we have found that using the stemmer for Croatian by Ljubešić and Pandžić alongside the POS-TF STSS approach yields the best results among the evaluated models.


Together with the SPADE, researchers objectively compare performances of their methods measured by ALIR and ALIP with those of others .

aLIR evaluates how gold-alignments can be replicated by automatic alignments and aLIP measures how automatic alignments overlap with alignments that at least an annotator aligned as: aLIR aLIP Human 90.65 88.21 (arase and Tsujii, 2017) 83.64 78.91 Table 2: Benchmarks of aLIR and aLIP on the SPaDE where Ha is a set of automatic alignments, while G and G are the ones that two of annotators produce, respectively.


TPO (union) is the union of all pairwise TPO agreement tables.

any phenomena that is annotated with the same label and the same scope by any 2 annotators is part of the TPO (union).


In this task, crowdsourcing workers were asked whether presented words denote concrete objects or may have emotions or senses.

we divided this phase into two sub phases.


We also attempted to gather those of sentence readers.

crowdsourcing workers were asked to answer one of feature changes of presented arguments in sentences (Figure 1 (a)) or those of the workers themselves (Figure 1 (b)).


First, we propose a method to obtain and quantify qualitative information relevant to a controversial issue by engaging people directly via crowdsourcing.

we create a comprehensive dataset of nuanced assertions relevant to sixteen controversial issues in the United States.


1 1 https://sites.google.com/view/you-on-issues In recent years, a number of web-based applications have been developed that help users share their opinions and explore public opinion on controversial issues.

voting Advice Applications such as votecompass.com, politicalcompass.org, and isidewith.com ask visitors whether they agree or disagree with a set of pre-chosen assertions (Garzia and Marschall, 2012).


News article headline published by La Jornada (originally in Spanish): "@lajornadaonline: He is not my president!, they shout in US cities after Trump's victory."

a sample of its replies (translated from Spanish): • @user1: @lajornadaonline @realDonaldTrump They are making pressure, and it is obvious that this is not necessarily going to change the state of things... • @user2: @lajornadaonline Polarization has generated radicalizations that threaten to rise tone • @user3: @lajornadaonline @realDonaldTrump democracy, accept it; it was a plot.


In addition to that, the integrated version of newspapers (see Figure 4) clearly shows that some emotions are less frequently expressed in replies to news articles.

love and surprise were not expressed at all (0%) in 229 and 189 articles respectively, it means that less than 40% of articles provoked those emotions.


• If a tweet / comment is marked as exhibiting General Non-threatening Aggression by the annotators then it cannot be marked for any other effect.

any comment can be marked as General Non-threatening aggression only if it does NOT contain any other kind of threat/aggression.


The use of the pre-trained embeddings model helps the model in property assignment.

domain-specific embeddings provide higher performance gain in the task-at-hand when it is used in both channels.


Following the success of the English WordNet, parallel networks have been developed for a variety of languages.

researchers from Italy have developed a methodology for parallel development of multilingual WordNets (Bentivogli et al.


Following standard practices for applying neural architectures to NLP tasks, we initialize our network with word embeddings trained on orders of magnitude more data than is available for our task (SRL).

we use publicly available GloVe 100-dimensional vectors trained on 6 billion words from Wikipedia and Gigaword (Pennington et al., 2014).


Even Imaginative WordsEye (3 to 0) 60.3% (126) 85.6% (179) WordsEye (2 to 1) 25.4% 53Google (2 to 1) 10.0% (21) 14.4% 30Google (3 to 0) 4.3% (9) Total 100.0% (209) Winner (votes) Realistic WordsEye (3 to 0) 8.8% (22) 16.4% (41) WordsEye (2 to 1) 7.6% 19Google (2 to 1) 14.4% 36   when Google had a better rating than WordsEye, Words-Eye still tended to win by votes.

out of the 42 cases where the Google image received a better rating, Turkers chose the WordsEye image for 24 (more than half) of them.


While this result is not unexpected, we can now quantify what the gap in performance actually is.

while the average rating of WordsEye on realistic sentences was just 0.665 below that of Google, WordsEye's ratings on imaginative sentences was 1.244 higher than Google's.


the absence of a synonym pair: this is a special case of the problem mentioned in the previous point as mapping M:1.

two or more words have the same translation into the target language.


Croatian an English corpuses used for the training have serious differences in morphological complexity according to regularities of the Croatian and the English language.

the difference in English (Germanic language) and Croatian (Slavic language) morphology is huge.


Excluding relations of place and time (which may apply to static or dynamic scenes), these fall under the CONFIG-URATION portion of the supersense hierarchy.

together, 1 : Annotator A annotated all genitive case markers in the corpus, and selected examples for discussion with Annotator B.


In this section, we propose several metrics which aim at finding an optimal configuration of all these meta parameters jointly.

to compare different configurations of our approach, we compare the labeled sense clusters to WordNet 3.1 (Fellbaum, 1998) and BabelNet 3.7 (Navigli and Ponzetto, 2012).


From the directed graph A, a Minimum Spanning Tree (MST) is constructed, using Chu-Liu-Edmonds algorithm (Chu & Liu, 1965;Edmonds, 1967) where the weight of each edge is 1/|V|.

we computed the tree that covers all the sentence and whose dependency relations are discovered simultaneously by as many of the individual parsers as possible (i.e.


The improvement margin can only increase when adding more, as different as possible, parsers into the mix that uses our supplementary linguistic features and we plan to extend this work by adding more such parsers to the ensemble.

we need to see what parsers from the CONLL 2017 shared task are open-sourced and readily available such that new ways of modelling dependency parsing could be of benefit to our ensemble parsing.


In contrast, industrial endeavors require a more holistic view; they need to work with given practical settings and address specific requirements of live systems.

we have identified three relevant perspectives to our project, all of which need to be considered simultaneously: The scientific/technical perspective focuses on questions such as which methods to apply for particular subproblems, how to best make use of the available data, which evaluation metrics to apply, etc.


For the implementation of this approach, we chose a problem in the sentiment annotation domain, where diverse judgments representative of several communities are valuable due to the subjective nature of evaluating sentiment in a given context.

we chose to collect judgments on the toxicity of Wikipedia discussion comments, where a "toxic" comment is defined as any kind of hateful, aggressive, or disrespectful comment that is likely to make someone leave a discussion.


As can be seen from the MOS box plots and Table 4, the baseline Javanese (jv) and Sundanese (su) systems bootstrapped solely from their respective multi-speaker corpora are inferior to the Indonesian system (id).

the Sundanese system is significantly worse.


One major characteristic of this type of objective propositions, as opposed to the nonexperiential counterparts classified as FACT, is that it is often practically impossible to provide objective evidence in online commenting setting, in the form of URL or citation.

evidence for TESTIMONY is not publicly available in most cases.


Our emphasis thus is on a multi-layer resource that allows for correlating different levels and for studying dependencies between discourse relations and argument structure, or -enabled by our new result -discourse relations and underlying inferential moves.

we propose guidelines for the annotation of argument schemes for both SUPPORT and ATTACK relations using the Argumentum Model of Topics framework (Rigotti and Morasso, 2010) (Section 2).


• Inferential rules: if the cause is the case, the effect is the case if the effect is the case, the cause is probably the case if a quality characterizes the cause, then such quality characterizes the effect too if the realization of the goal necessitates the means x, x must be adopted if an action does not allow to achieve the goal, it should not be undertaken if somebody has the means to achieve a certain goal, he will achieve that goal Compared to the previous version, although the taxonomy and related theoretical insights have been maintained, the definition of the argument schemes that gave rise to the highest disagreement in the previous study have been clarified pointing to distinctive features.

the CAUSAL argument schemes (from means to end) that we mentioned above have been frequently annotated by others as PRACTICAL EVALUATION, described as follows: • Identification Question: does x express an evaluation about a state of affairs and does y express an advice/a recommendation about stopping/continuing the state of affairs the premise refer to?


Even though the available data are not enough to draw generalizations, we consider them as hints to be used to refine the guidelines.

we plan to stress on the differences between the propositional and the inferential levels of analysis.


In our present corpus-based case study we focus our attention on the issue of local coherence as established by links between the thematic (Topic) and rhematic (Focus) parts of sentences.

we want to verify if the classical observations valid for English as a language with a grammatically fixed word order, namely that there is a prevailance of "constant theme" (based on Mathesius´ 1947 study on the thematicity and "continuity" of English subject, and further analyzed esp.


However, these three factors are highly unlikely in this example because each descendent form shares the same meaning and a similar phonetic form.

each word begins with a 'd' sound and it contains a consonant cluster 'nt'.


We decided to test whether six language families show greater rates of change in consonant inventory size as compared to vowel inventory size using phylogenetic comparative methods.

we use BayesTraits V2 (Meade and Pagel, 2014), which implements a generalized least squares approach to modeling the evolution of continuously varying traits (Pagel,   Table 1 gives the mean rates of change of consonant and vowels on the branches of the listed phylogenetic tree sets 6 We also observe that ancestral state estimates of vowel and consonant inventory sizes are generally closer to the mean of the range than expert reconstructions of proto-languages.


In future work, we plan to enrich SentEval with additional tasks as the consensus on the best evaluation for sentence embeddings evolves.

tasks that probe for specific linguistic properties of the sentence embeddings (Shi et al., 2016;Adi et al., 2017) are interesting directions towards understanding how the encoder understands language.


The novelty of the hybrid framework we describe here is that we exploit state-of-the art learning methods while also incorporating features based on what we know about the linguistic composition of lexical signs.

we analyze hand shape, orientation, location, and motion trajectories, and then use CRFs to combine this linguistically significant information for purposes of sign recognition.


We have established a framework for sign recognition that relies on combining 3D modeling of start and end handshapes as distributions based on a few initial and final frames (enhanced by statistical information about their linguistic dependencies) and of 3D movement patterns of the hands, arms, and upper body during sign production.

we have developed a statistical approach that combines distributions of the initial and final hand shapes and pose coupled with the spatiotemporal patterning of the arm and upper torso.


In this paper, we propose a sequence-to-sequence deep learning model to address the news image caption generation problem.

we first encode each sentence of a given news article using an order-embedding vector and extract semantic features from the accompanying image using a pre-trained CNN Network, which are further projected to same semantic space, such that both text and image vectors reside in a common semantic space (Vendrov et al., 2015).


Therefore, the models should be designed such that there is strong correlation between phrases and images or sentences and images that are semantically relevant.

a direct leap is taken from image to sentence and vice versa.


All systems apply the same setup with the same number of training batches without any language-specific tuning of any of them.

we use 256 dimensions for word embeddings, 512 dimensions for the hidden LSTM-layer in the encoder, 1024 dimensions for the decoder LSTM's and 256 dimensions for the attention layer.


Event detection detects either emerging events or specific events from raw stream data.

the large amount of work done on Topic Detection and Tracking (TDT) attempts to detect emerging events.


Similarly, reset gate allows the model to drop information that is irrelevant in future.

a GRU network successively reads the input token t i , as well as the previous hidden state h i−1 , and generate the new memory content c i and hidden unit h i .


In this paper, we investigate whether authors are located or not located in the locations they tweet about, and temporally anchor this spatial information in the tweet timestamp.

we work with temporal tags centered around the tweet timestamp: longer than 24 hours before or after tweeting, within 24 hours before or after tweeting, and at the time of tweeting.


We found that no spatial relationship can be inferred between authors of tweets and the locations they tweet about in 21% of instances.

1 out of 5 locations in a tweet do not indicate any spatial information about the author.


we discuss the quality in terms of inter-annotator agreement and the label distribution.


Note that disagreements are relatively minor: most disagreements are between CY and PY, CY and UNK, CN and PN, CN and UNK, or UNK and INV.

while annotators do not agree all the time, the sources of disagreements are not a major source of concern.


In this paper, we present a corpus of tweets annotated with temporally-anchored spatial information involving the author.

we annotated whether authors of tweets are located in the locations they tweet about when they tweet, within 24 hours before or after tweeting, and longer than 24 hours before or after tweeting.


» (TEI 2018 : att.lexicographic) ; status: optional; datatype: teidata.text 10 We propose that this attribute should also be available within the class att.linguistic.

we do not postulate an introduction of a new device, but merely an extension of the structural context in which an existing TEI attribute may be used, keeping its definition and data type intact.


Note that the wrappers will be required to monitor the execution of the tools they wrap.

when an HTTP POST request yields an error message, then this message is sent back to the WebLicht orchestrator, which then prematurely terminates the execution of the workflow with an appropriate error message.


On the other hand, as shown by Chen and Manning (2014), the performance of the Malt parser is only about 1.5-2.5 points below the neural-based Stanford parser.

we used the stack model based on the projective transition system with the  Table 3: An excerpt from an output document in the CoNLL format: a document header plus a sentence are shown.


We use Rhetorical Structure Theory (Mann and Thompson, 1988) as the theoretical framework of the corpus.

we develop our annotation guidelines based on the guidelines used in the Potsdam Commentary Corpus (Stede, 2016).


It aims at precise representation of the structure of lexicalised arguments.

it is used to represent all acceptable dependants of complex prepositions.


Preliminary analysis has shown that these sentences exhibit several problems including errors both in Składnica and in Walenty.

for some verbs the two dictionaries differ whether a given dependent should be considered a complement or an adjunct.


In these calculations we use the relative number of all orphan nodes for every team, which is based on alignment between system output words and gold standard words.

only successfully aligned orphan nodes from gold standard are included in this number.


Another example of a filtering rule is the requirement that the heads of both clauses have at least 3 dependents that are neither function words nor punctuation.


In the present paper, a new semi-automatic approach for the construction of word-formation networks is presented.

it applies sequential pattern mining to construct useful morphological features.


We selected our pre-processing architecture based on parsing results on the development sets.

we chose to use either this new tagger or the UDPipe baseline provided by the organisers.


Some of the extracted paradigms from Wiktionary were subject to additional post-processing.

some Wiktionary tables contain multiple forms in the same cell.


The provided meta-information about cited publications is often not accurate.

the information about the title, the authors, and the venue of cited publications are sometimes incorrectly segmented.


Assuming this difference is not directly linked to the test corpus, and besides the above disambiguation issue, we consider English linguistic specificities as an explanation.

links between items of a multi-word spatial entity are seldom made explicit, neither by a common morphological feature, nor by a linking preposition.


To address the issue of individual variation, we introduce the notion of a speaker's individual portrait.

we consider the Prosodic Portrait, the Oculomotor Portrait, and the Gesticulation Portrait.


Second, the content of various communication channels traditionally belongs to the competence of different disciplines, weakly connected with each other.

the verbal component is studied by linguists, while gestures and eye movements are explored, primarily, by psychologists.


Many prosodic phenomena are of a relative, rather than absolute, nature: their specific realizations can be assessed and identified only with respect to neutral characteristics of a given speaker's voice.

two kinds of the falling intonation must be distinguished: a final falling (so-called period intonation) and a non-final falling (a falling comma intonation).


In order to posit punctuation marks in a transcript belonging to a certain speaker, one needs to explore and describe the speaker's prosodic system.

a complete account of a spoken discourse presupposes a speaker's Prosodic Portrait, i.e.


For example, we observe that the connectives sometimes fulfill roles other than linking two text spans semantically.

the connectives but, so exhibit a range of functions that could be defined as sequential linkage (speech management, topic structure, etc.)


As shown in Figure 1, the relations Elaboration, Background, and Joint are non-leaf nodes in the logical semantic structure tree of the chtb 0019.

p2 elaborates the background of "Ningbo Bonded Area", which forms a Background relation together with p1.


In addition to the logical semantic structure, we also define the functional pragmatic structure.

on the basis of the logical semantic structure, we add function pragmatic attribute to each node.


2 uses the D3.js 2 library for visualizations.

it was designed for annotating RST trees (Mann and Thompson, 1988)  TREEANNOTATOR's visualization addresses current workflow needs more specifically than comparable tools: (a) a second mode of visualization is available, independent from classical RST trees; (b) L A T E X output for scientific publications can be generated; (c) a multiple window view to inspect annotations side by side is provided.


By integrating UIMA DI into ResourceManager, any UIMA document becomes accessible as a resource and organized in projects, which are defined as repositories.

user rights can be inherited from higher-level groups via restriction or extension in order to specify resource-specific permissions.


The dimensions of chat and chunk durations observed would indicate that social talk should 'dose' or package information to fit chat and chunk segments of roughly these lengths.

the tendency towards chunks of around half a minute could help in the design of narrative or education-delivering speech applications, by allowing designers to partition content optimally.


To improve the applicability of advanced NLP techniques, we need to experiment and systematically study the performance of such techniques on historical texts.

we must understand if-and in what wayancient languages behave differently than contemporary languages when they are transferred and reused, especially when reused paraphrastically (where the reuse is not a literal copy of the source).


Therefore we investigate to which extent crowdsourced data are suitable for the development of human-like SDS.

we want to provide answers to the following questions: • Do data elicited via crowdsourcing reflect real system usage and naturalness?


The mean tree depth of the system-directed utterances is 2.94 and of the crowdsourced utterances 2.33.

people tend to use more complex syntactic structures when talking to an actual system.


In this paper, we thus propose a new reasoning task and its evaluation dataset, called Visual COPA (VCOPA) as a variant of COPA, which covers the visual questions for commonsense causal reasoning.

figure 1 illustrates an example of VCOPA question, which is converted from a textual question on COPA (Example 1) into its corresponding visual question with three images.


Causality scores depend on the number of occurrences that two terms t i and t j are in linguistic patterns known as causal cues (Chang and Choi, 2004) identifying precise cause/effect roles, e.g., "If...t i ..., then... t j ..." and "... t j ..., because...t i ...".

as more occurrences of (t i , t j ) in causal cues, its causality score is higher as: where f req(t i , t j ) is the frequency of observing the causal pair (t i , t j ) from an English corpus.


We show that in our scenario, given the choice of three systems with varying F-scores, the system with the highest F-score does not yield the highest profits.

we show that the cost-benefit trade off can be more favorable if a system with a lower F-score is employed.


Hence, these measures provide insight that is useful for a number of different annotation scenarios.

there are three scenarios where we argue that gain and cumulative gain can be invaluable for the decision-making process: 1.


the sequence in which they are given to the annotator, becomes very important.

although two models that return the same number of correct predictions will be judged as identical using classification accuracy metrics, these models may still differ in terms of the distributions of true positive and true negative (i.e.


(Situation 2) We have a flexible budget and we can afford to annotate a variable amount of deciles, but we still want to spend as little money as possible, yet find the maximum number of VNICs.

we are looking for the model that can yield the maximum number of VNICs in the earliest decile.


We keep in mind that the gain scores scale to different sizes of datasets: if the model had a gain of x on the third decile of the test set, we can extended our annotation to cover the third decile of the project data as ordered by the model, we would be likely to have a similar gain on our larger dataset and ultimate linguistic resource.

just like with gain, we can extrapolate from the cost calculations on our test set to estimate the cost of annotating a larger-scale unlabeled dataset.


As such, this paper is concerned with generating multilingual syntactic analogy datasets for intrinsic evaluation of word embeddings.

we present a method of leveraging existing resources such as dependency parsed corpora to automatically generate the datasets.


However, though relatively recent, dependency grammars are an extension of this approach that offer several advantages to the former.

dependency grammars' independence from a static word order allows a number of typologically varying languages to be properly represented.


Regarding false negatives, there are two main reasons for our system to miss a biomedical concept: on the one hand, it can happen that the concept is not captured in the Metathesaurus at all; on the other hand, it could be that the concept is captured but not as expressed in the text, be it because it is misspelled, abbreviated in a way that the Metathesaurus does not contemplate, or formulated in any other non-standard way.

false negatives are caused by a poor lack of the Metathesaurus and the lexical variability of clinical narrative.


Taxonomy learning is a task that has up until now not achieved satisfactory results.

research on terminology extraction using has not produced a higher F-Measure than for a very simple baseline of whether a substring is included in the term.


And there is a prefixed symbol either '+' or '-' depends on the direction of the relative relationships between temporal entities.

the relValue should be start with '+' symbol if the meaning of text is later than the reference date, and vice versa.


As a final note, the LAPPS Grid, Galaxy, and PubAnnotation are all open source projects that invite contributions from developers and users.

the LAPPS Grid seeks the contribution of tools and resources for biomedical text mining to augment the current facilities.


For convenience, we used the LDC conversations as the unit of annotation for relevance judgments.

our evaluation asks whether we are able to find a conversation that the searcher might wish to see.


Another task of the SemEval campaign deals with semantic similarity in the context of Community Questions Answering (Nakov et al., 2017).

: one subtask addresses "question-question" similarity, where the questions are questions posted on an english-speaking forum (Qatar Living Forum) which deals with any aspect of daily life for foreign people in Qatar.


On the Czech part of the corpus, our last results reported in (Švec et al., 2017) achieved a word Accuracy (Acc) of 80.89 We used the same acoustics model (AM) as in (Švec et al., 2017) in the experiment.

we have followed a typical Kaldi (Povey et al., 2011) training recipe (Kamper et al., 2016) for a Deep-Neural-Network-based AM training.


This means that a larger negative value does not automatically mean that there is a bad effect of this modality but solely that in conjunction with at least one better performing modality, the contribution to the overall result was moderate.

the reason why ASD values suffer if the negatively correlated modality gets stronger may be the result of the more effective ones getting weaker not necessarily because of a bad fit of the modality itself.


Participants were given a spatial scene description task without instructions on how to perform the task.

they described intuitively, either only using speech or using both speech and gestures.


(2013), expand on their objective functions and propose new architectures fusing these objective functions.

the Deep Canonical Correlated Autoencoder (DCCAE) uses both objectives from previous works simultaneously to increase correlation between obtained features and fidelity to original data.


These and other resources provide data that is annotated on several linguistic levels, but do not lend themselves specifically to research on adjectives.

we are not aware of any freely accessible corpus query tools that offer the possibility to easily filter the corpora according to complex syntactic or morphological (e. g. derivational) criteria.


These tools have some great features but also lack key features which do not make them completely suitable for annotating MRLs.

changing the tokenization of a word is not easy to do in these systems.


We plan to continue enhancing PALMYRA's capabilities.

we are interested in linking it to a state-of-the-art Arabic parser (Shahrour et al., 2016) to support online syntactic analysis.


However, this work left out the analysis of some interesting phenomena.

the presence of clitics in the corpus was marked but not properly analyzed, as well as the use of relative clauses as modifiers of noun phrases.


In addition, the guideline suggests some lexical categories to have sub-classes.

nouns (verbal noun -VN), verbs (auxiliary -AUX, relative verb -VREL) and numerals (cardinal -NUMCR and ordinal -NUMOR) which have sub-categories with the respective specific tags.


An XMG2 compiler takes as input a metagrammar, and produces the lexicon of all the structures described in it.

it converts a compact representation of a resource into the resource itself.


One of the characteristics of this model activity is that the procedure is the same as conventional presentation practices.

the required time for the observation in Step 2b is the same because students observe the practices in real-time and use the video data following Step 2b.


Recent progress in infant speech database collection (Xu et al., 2008;Roy, 2009;VanDam et al., 2016;Casillas et al., 2017;Warlaumont et al., 2017) and computational modeling of developmental processes (Martin et al., 2016;Ludusan et al., 2015;Carbajal et al., 2016a;Carbajal et al., 2016b;Ludusan et al., 2017) open up possibilities to measure and model the progress of children using data collected in their natural environment.

it becomes technically possible to offer parents scientifically grounded analytics and tools to explore and document their child's progress.


Other plugins will be added as they become available in open source format.

we are working in collaboration with the ACLEW 3 consortium.


All studies in Section 4 are performed on content tokens only.

we exclude punctuation and digits (non-content tokens) from our calculations, which make up 18% and 15% of all tokens in the curricular and fiction subcorpora, respectively.


As a result of our effort, seven new linguistic processors for Galician are presented: a rule-based tokenizer and statistical tools for POS tagging, lemmatization, Named Entity Recognition, Named Entity Disambiguation, Wikification and graph-based Word Sense Disambiguation.

this paper presents the first open source statistical lemmatizer and Named Entity tagger for the Galician language.


IXA pipes modules consist of: (i) Local, shallow features based mostly on orthographic, word shape and n-gram features plus their context; and (ii) three types of simple clustering features, based on unigram matching.

iXA pipes implements, on top of the local features, a combination of three word representation features: (i) Brown (Brown et al., 1992) clusters, taking the 4th, 8th, 12th and 20th node in the path; (ii) Clark (Clark, 2003) clusters and, (iii) Word2vec (Mikolov et al., 2013) clusters, based on K-means applied over the extracted word vectors using the skip-gram algorithm.


In this paper we presented a new set of linguistic resources and NLP tools for the Galician language.

we would like to highlight the contribution of two new manually revised and annotated corpora for Galician POS tagging, lemmatization and Named Entity Recognition (NER).


Thus, we applied a guideline that focuses on text-level polarity, abstracting away from the (opaque) writer's intent.

annotators were asked to judge the sentiment of the phrases mainly by the polarity of the vocabulary encountered in them and regardless of pragmatics, where possible.


In addition, (II) is important to maximize the number of disease names that were not yet collected from other language resources.

it is important to expand the vocabulary in J-MeDic.


However, in this list, the polysemy of compound verbs was not taken into account.

the synonymous expressions of compound verbs were stored together without distinction of polysemous meaning in this list.


There are antonymous expressions like 水を打ったように静かになる (mizu-wo uttayouni shizukaninaru, hush fell over) and かすかに流 れる (kasukani nagareru,, flow faintly) and also synonymous expressions like いっぱいにあふれる (ippaini afureru, full of and overflow) and あふれて出る (afurete deru, overflow and spill out).

the expressions in Cluster 3 are not separated clearly.


On the other hand, learning to associate trigger words and frames is the easiest part of the frame detection problem; disambiguation is the hardest.

if we assume that we can solve the disambiguation problem with more data, we should be able to trivially learn which frames are adequate for each verb.


To count the frequency of these idioms we expand the generic pronoun with the various pronouns for person, number, and gender that we may encounter in the corpus.

in the case of possessive adjectives, we replace occurrences of one's, anyone's, or someone's with my, your, his, her, our, and their.


The use of Linked Data and Semantic Web technologies in applications delivers structured information, which can be used and queried by a flexible and an extensible way to get a better understanding of the data.

the platform exploits the Semantic Web model of data types, to describe the possible format of input and output to services.


The use of Semantic Web technologies will help Teanga, as a platform, to understand the data input and output for each of the services added to the system, and will contribute to creating data interoperability among services to create clear and straightforward pipelines when the user needs to use them.

there have been a number of models for the representation of linguistic structures used in natural language processing as linked data.


A key enabling factor for the Teanga platform is the use of the best technologies that exist, in order to enable the service to work efficiently at scale.

we make use of the following open-source tools: 1.


The export must be dynamically configurable, so that the same NLP pipeline can drive the generation of different triples in the same, or multiple different, knowledge bases.

the vocabularies used in the mapping process must be easily changeable, to support experiments with different knowledge bases and different application scenarios.


A core idea of our approach is that the concrete mapping, from NLP results to knowledge base, is not part of the NLP pipeline itself: Rather, it is externalized in form of a set of mapping rules that are encoded in the knowledge base itself.

only by dynamically connecting an NLP pipeline to a knowledge base is the concrete mapping effected: The same pipeline can so be used to export different results, using different mapping rules, to multiple knowledge bases.


Additionally, we can export some pre-defined relations between entities into triples.

we can map a contains relationship (based on text offsets) to any LOD vocabulary.


In some respect, LODeXporter is our completely re-designed approach for knowledge export from NLP pipelines, based on the experience we gained from building numerous knowledge-intensive applications.

with LODeXporter we focus on scenarios that were less common when we first designed OwlExporter, more than 15 years ago: Large-scale knowledge bases, generated in distributed cloud-based environments, linked data datasets and applications (based on the LOD initiative started in 2007), shared open vocabularies, as well as the proliferation of AI applications, such as intelligent assistants, where NLP techniques form just one of numerous knowledge sources and algorithms.


The terminological and technological foundations described in this paper represent the basis for the future publication of cuneiform corpus data and their annotation, developed within the MTAAC project.

this includes the major administrative and legal Sumerian corpus of the Ur III period (2100-2000 B.C.)


The paper focuses on the main feature (interoperability) that represents the specificity of PMKI platform distinguishing it from other existing frameworks.

it aims to create a set of tools and facilities, based on Semantic Web technologies, to establish semantic interoperability between multilingual lexicons.


For instance, such platform is missing in CEF.AT 2 , while it would provide an advantage for the development of machine translation systems.

it can provide alignments of domain specific terminologies for developing specificdomain translation systems (tender terminology, medical terminology, etc.).


This paper presents the preliminary results obtained within the PMKI project for implementing interoperability solution of lexical resources.

the main Semantic Web standards available in literature for representing lexicons have been identified and their characteristics were analysed.


This lack of accurate categorization of MWE makes linking data sources such as LIDIOMS with other resources very difficult.

using declarative link discovery frameworks for computing similarities among MWE without the right classification information becomes a slow task which leads to links with a low level of precision.


We would like to thank all native speakers which contributed with this work.

maria Sukhareva from the Olia Project 15 , Will Hanley 16 , Chiara Pace and Devayani Bhave.


Here we argue that commentaries for an extensive-form game (e.g., chess) are an ideal testbed for developing truly intelligent systems.


To this end, we annotate a shogi corpus for a commentator's confidence in this work.

we adopt a three-layer annotation scheme: modality expression, event class and factuality.


For Chinese light verb, we use a simple test to examine this property.

if the light verb can be omitted without changing the proposition/semantic meaning of the construction, we consider the verb itself adds no semantic and is semantically bleached.


In English, for example, The Queen paid a visit to the Prime Minister + a visit of the Lady to the Prime Minister →*The Queen paid a visit of the Lady to the Prime Minister.

the visitor cannot be a modifier of visit.


We use reference corpora (RC) to check word frequencies and co-occurrences.

the algorithm makes use of two RCs, a Spanish RC and an English RC.


This section summarizes our findings regarding the production of errors outside the context of collocations.

we consider orthographical, grammatical, lexical, punctuation and discourse marking errors.


Let D = {d1,d2,••• ,dl} be a set of l text segments (local contexts), each containing a target VNC (i.e., σvn).

instead of generating a term by document matrix, where each term is tfidf (product of term frequency and inverse document frequency), we compute a term by document matrix MD ∈ ℜ m×l , where each term in the matrix is the product of the projection of a word onto a target VNC and inverse document frequency.


Instead of generating a term by document matrix, where each term is tfidf (product of term frequency and inverse document frequency), we compute a term by document matrix MD ∈ ℜ m×l , where each term in the matrix is That is, the product of the projection of a word onto a target VNC and inverse document frequency.

the term frequency (tf) of a word is replaced by the projection of the word onto σvn (1).


In this representation, each term is a product of p and typical tf-idf.

(3) Our second hypothesis states that words in a local context of a literal expression will have a different distribution from those in the context of an idiomatic one.


The results of the experiment suggest that for Russian our algorithm performs similarly to English, even considering the fact that Russian is a more morphologically complex language and has a relatively free word order.

the results demonstrate that one of our proposed methods -CoVARFro performs with highest average accuracy for precision and recall measures.


The word-level distribution features treat a concept as the set of words composing its article title.

the relatedness between concepts is computed as follows.


(2010), we also formalize the word segmentation problem as a sequence labeling task.

each syllable is labeled by either segmentation tag B (Begin of a word) or I (Inside of a word).


Table 3 shows that RDRsegmenter obtains the highest F 1 score.

rDrSegmenter obtains 0.5+% higher F 1 than vnTokenizer (Le et al., 2008) though both approaches use the same lexicon for initial segmentation.


Albanian has a rich morphological system and a relatively free word order, similar to, for example, German.

challenging, from a pos tagging point of view, are the many multi-word units.


Albanian is one of the Balkan languages with the least resources available.

there is no part-of-speech tagged corpus available, 1 let alone one that covers the multiword phenomena mentioned above.


In our own previous work (Kabashi and Proisl, 2016), we introduce a medium-sized tagset for Albanian that consists of 67 tags and that aims to adequately represent the morphosyntactic properties of the Albanian language.

combinations of preposed articles or particles with words of other word-classes are treated in a linguistically sensible way.


We also renamed some of the existing tags to have a more consistent and transparent naming scheme.

we make the following changes: • We introduce four new verb tags: VImpv (imperative form of verb), VImpvCl (imperative with infixed clitic), VSubj (verb with subjunctive particle) and VSubjPass (verb with subjunctive and passive particles).


The design of our interface borrows heavily from the design of MANDIAC (Obeid et al., 2016).

we utilized the client-server architecture, as well as the flexible hybrid SQL/JSON storage system used by MANDIAC.


While these values attest to the coverage of the analyzer, its efficiency and accessibility was further assessed by examining its output for all parsed words.

the average number of analyses generated per word was 20, while the median was only five, a discrepancy that suggested some words in the end-of-chapter exercises were generating an unusually high number of analyses, thus distorting the average.


We have already identified several gaps in the documentation concerning certain Yupik linguistic phenomena, including aspects of the demonstrative and numeral systems.

the reference grammar presents demonstrative inflection as a series of eleven "paradigmatic sets", and within each set, demonstratives take on an additional six base forms that dictate the inflected form (Jacobson, 2001, p.110).


We describe the development of UniMorph resources for languages of the Caucasus region, known for its rich and diverse arrays of languages and language families, and often posing challenges to European-centered views established in traditional linguistics.

we focus on Nakh-Daghestanian (North-East Caucasian) and Kartvelian (South Caucasian) languages, as well as on Classical Armenian, and discuss challenges that these and related languages pose to the current design of UniMorph.


assumptions about case morphology and its relation with subjecthood are taken into consideration.


One key advantage of a future RDF vocabulary of Uni-Morph data in comparison to TSV data would be that additional data can be added as needed, without affecting its processability.

it may preserve any information from the original, resource-specific RDF -just cleanly separated in a distinct namespace.


Another difference between the two parts of the corpus is that, even if movies reviews are generally longer than books reviews, they are less dense.

we noticed that book reviewers are more creative in language expression than movie reviewers, as it is possible to see from the table 4 that shows the ratio between the total number of words (token) and the unique words (types) on the different parts of the corpus divided per type and rating.


We started by merging available sentiment dictionaries.

we merged items from The Harvard General Inquirer (Stone and Hunt, 1963), MPQA (Wilson et al., 2005), and Bing Liu's Opinion Lexicon (Hu and Liu, 2004), which we reduced to a common format that included simply the lemma or form and its associated polarity.


(2) Collect HTML files of web pages from the retrieval results for each topic.

the pages in the retrieval results from forum sites, review sites are collected.


A2 selects tweets to be more direct in 11 cases out of 13 while A1 does so in 10 out of 13.

in 11 out of 13 cases, A2 states that for a given pair, the tweet is more direct in its ridicule than the book snippet.


The obtained gold standard is now being used to train and compare different lexicon-based algorithms for creating a complete sentiment lexicon for Swedish.

we have experimented with growing sentiment lexicons based on a set of initial items and the lexical-semantic network structure of resources such as SALDO, plus contextual information from large corpora.


An alternative to sequential coding is a so-called open bigram encoding (Grainger and Van Heuven, 2004;Schoonbaert and Grainger, 2004;Grainger and Whitney, 2004), which is formed by taking the ordered 2-combination of each letter in a word.

the open bigram encodings of the words 'salt' and 'slat' are {sa, sl, st, al, at, lt} and {sl, sa, st, la, lt, at}, respectively.


These featurization techniques, which offer a better fit to empirical data, are not implemented in wordkit because, rather than being featurizers, SERIOL and SOLAR are actually full-blown models of letter recognition.

sERIOL and sOLAR do not produce the type of vectors which can be used as input by other models, and are more like the models that accept the input vectors which are generated by our transformers.


Note that, while these featurization techniques solve the problem of aligning phonological strings, they do not solve the problem of aligning syllabic units.

while phonemes within syllables are reasonably aligned when using both the CV and ONC featurizer, there is no guarantee that syllables themselves are aligned.


In addition, we use our own observation for Koine Greek: If a question in Koine Greek is not translated as a question in the other languages, but as a declarative, we assume that this question is most probably a NISQ.

if the translators chose to not translate it as a question, then the question is not asking for information but is rather accomplishing other communicative goals.


For instance, the implicit condition "living environment", which is the second most frequent by annotator A in Table 4, can be fulfilled differently in terms of the field values of properties depending on the user.

a good property for a user is not always relevant for other users.


These mistakes reflect technical limitations that exist in current state-of-the-art systems and that therefore often occur during a conversation with an autonomous agent.

we created the following unexpected situations: Noise.


One striking observation of Human-Human (H-H) Interaction is that the communicative behaviors of dialog participants (DPs) tend to converge (Gallois et al., 2005) and automatically align at several levels (such as the lexical, syntactic and semantic ones) (Pickering and Garrod, 2004).

dPs tend to reuse lexical and syntactic structures (Reitter et al., 2006).


Recent work proposes automatic verbal alignment measures based on the repetition between speakers at the lexical level (Dubuisson Duplessis et al., 2017b).

they focus on which words and lexical patterns are shared between dialog participants.


This shows 9 Relative shared vocabulary for S1is computed as follow: a tendency towards a symmetrical verbal alignment, which is closer to what has been previously observed in H-H Negotiation Interactions.

this indicates a more "human-like" verbal alignment in the Alice corpus compared to the H-A Negotiation corpus.


In IQA, a generated pseudo dataset that comprises triplets consisting of an image I, question Q, and answer A should satisfy an important constraint: the components of a triplet (I, Q, A) must be relevant to each other.

the answer A must be inferable from an image-question pair (I, Q).


Although the improvement is not always significant, it becomes more prominent when the size of Seed data is small.

our method decreases the test error by 1.24% in the case of the annotated data of size 75K.


We study why such approach fails and motivate needs to build seed resources to enable leveraging such resources.

we annotate seed QA pairs of small size (4K) for Korean language, and design how such seed can be combined with translated English resources.


This score will be high if our weak QA finds the right answer, which might be near impossible without reasonable translation quality.

selecting QA pairs with high score would ensure high translation quality of the data.


So, 8BPS is \"Adobe Photoshop\".
\n\n
8B crops up in quite a few places in Adobe file extensions or internal types.

Wikipedia has a list.
\n
", "answerer": "Syjin <https://stackoverflow.com/users/733368/syjin>", 23 "answererLevel": "1,922", "answerVotes": 0, 25 "answerText": "just the Signature to identify the file as a Photoshop file.


Unlike search engine instead of extracting information, here a QA system usually focus on extracting relevant and precise answer(s).

we can say QA system is the extended modification in the search engine.


When they formulate a question by looking a paragraph in one (English or Hindi) article, they also have to verify whether the question of interest is available in the comparable article or not.

they have to provide the question, answer, answer source (sentence or paragraph where the answer exists), type of question (factoid, descriptive) in both the languages, if exist.


Word embedding is generated through pre-trained Google news word embedding model.

all the deep learning models use the 300-dimension word embedding, feature map size of 300 on multiple filters with window sizes of 3, 4, 5.


The paper hereby presented namely focuses on how this annotation scheme was designed and applied to the corpus.

also comparing the annotation produced by CrowdFlower contributors and by expert annotators, we make some remarks about the value of the novel resource as gold standard, which stems from a preliminary qualitative analysis of the annotated data and on future corpus development.


Despite the vast amount of proposed algorithms, the evaluation of recommender systems is very difficult (Fouss and Saerens, 2008).

if the system is not operational and no real users are available, the quality of recommendations must be evaluated on existing datasets, whose number is limited and what is more, they are focused on specific domains (i.e, music, movies, etc.).


In each case we find the page for the speaker with a novel method for finding gender-labeled speakers from Wikipedia.

our method takes as input a Wikipedia category page such as https://en.wikipedia.org/wiki/ Category:American_female_tennis_players, and for each name listed runs a search for public pages using Facebook's Graph API.


Furthermore, the successes of neural models for many tasks over the last few years have intensified the interest for studying TL for neural networks.

tL was largely exploited in computer vision using pre-trained CNNs to generate representations for novel tasks; some of the parameters learned on the source dataset are used to initialize the corresponding parameters of the CNNs for the target dataset.


Cross-task TL performances In order to understand how cross-task TL improves POS tagging performances on Spanish, German and Italian social media content.

ly, which POS tags benefit more from transferring knowledge from NER task.


This will pose a challenge in terms of their effective modelling.

since the majority of code-switch bigrams occur only once in the training data, it will in general not be possible to  A code-switched bigram consists of two tokens.


In contrast, the CRV retrieval system proposed in this paper can retrieve speech corpora utilizing speech-specific retrieval conditions; another advantage is that users can manipulate the system visually and that they can add or modify retrieval conditions interactively.

it is useful when users do not know the corpus name and when they want to search corpora suitable for their own objectives or experimental conditions.


Since we do not have (morpho-)syntactic gold standard annotations for this release, we post-processed the automatic system output to improve its usability, but without actually changing or correcting it.

following the idea of a silver-standard, we introduced additional confidence estimations as meta annotations for individual predictions based on the agreement between different systems (see Figure 2a for an example of trees predicted by three different parsing systems for the same sentence and Figure 2b for corresponding global confidence estimations).


Manual annotations were conducted on the interviews of the gold standard part of the collection.

the manual annotations are additional annotations (not corrections) besides the automatic annotations.


We will therefore provide additional merged versions of dependency trees.

we will include a majority decision of the dependency parsers under tree constraints.


The latter is crucial to conduct large-scale non-expert annotation.

such representation should enable non-constrained interactions were the dialog structure is not predefined around a specific scenario with a single task to solve, but transitions between dialog states are learned from data.


As described in the previous section, we used the 22,357 reasonable dialogues.

each of the 22,357 replies was regarded as a positive example S + r of the corresponding trigger utterance S t .


In comparison, general dialogue acts are more effectively used in these tasks because the approach of using them separates the functions and content of dialogue and only focuses on modeling dialogue functions.

the approach of using generalized dialogue act classes does not have the problem of exponentially increasing the number of dialogue states, which is caused by combinations of dialogue content.


Researchers have tried to generate nods during speaking from speech and natural language.

they used several acoustic features, such as sound pressure and prosody, for generating nods (Yehia et al., 2002;KG et al., 2004;Graf et al., 2002;Busso et al., 2007;Beskow et al., 2006;Iwano et al., 1996;Ishi et al., 2010).


Though the development of multimodal and multiparty corpora is not a new domain, this corpus serves to fill in the gap in the investigation of the factors that influence collaboration and task success in a three-party setting and in providing tools for measuring group success.

the corpus will be exploited to investigate the factors that contribute to collaborative behaviors, e.g.


The expectations of a user interacting with such interfaces would be that the interaction is ideally as natural as possible.

users expect that the interface can understand who's talking, recognize signals of human behavior, understand them, coordinate the discussion and address humans in a natural form and at the correct time, as a human would do, facilitating overall collaboration on a common problem (Bohus and Horvitz, 2010;Al Moubayed et al., 2012).


The platform is semi-autonomous because some modules of the system are automatic (for example the dialogue generation) where some others are manual.

the speech recognition and the comprehension modules are simulated by a human: the doctor verbal production is interpreted in real time by the operator which selects the adequate input signal to be transmitted to the dialogue system.


We can learn this by watching the video, although we may not realize it if we rely on speech itself for our understanding.

such external speech factors as situation, cultural background, and speaker may affect word choices and speech meanings.


In the first step, we generated the lexicalized rules that would be used in the semantic parsing (offline part).

we relied on the schema independent rule templates to automatically extract the lexicalized rules from the schema annotation file.


Being motivated by this observation, we proposed a decomposition method based on analyzing the comparative expressions and their arguments.

we first developed a syntactic based method to detect these comparative expressions.